class_docstr: Reaction Featurizer.
  
  RxnFeaturizer is a wrapper class for HuggingFace's RobertaTokenizerFast,
  that is intended for featurizing chemical reaction datasets. The featurizer
  computes the source and target required for a seq2seq task and applies the
  RobertaTokenizer on them separately. Additionally, it can also separate or
  mix the reactants and reagents before tokenizing.
  
  Examples
  --------
  >>> from deepchem.feat import RxnFeaturizer
  >>> from transformers import RobertaTokenizerFast
  >>> tokenizer = RobertaTokenizerFast.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
  >>> featurizer = RxnFeaturizer(tokenizer, sep_reagent=True)
  >>> feats = featurizer.featurize(['CCS(=O)(=O)Cl.OCCBr>CCN(CC)CC.CCOCC>CCS(=O)(=O)OCCBr'])
  
  Notes
  -----
  - The featurize method expects a List of reactions.
  - Use the sep_reagent toggle to enable/disable reagent separation.
      - True - Separate the reactants and reagents
      - False - Mix the reactants and reagents
code: def __call__(self, *args, **kwargs) -> np.ndarray:
      features = self.featurize(*args, **kwargs)
      print(type(features), len(features))
      return features
  
file_path: deepchem/deepchem/feat/reaction_featurizer.py
imports: from deepchem.feat import Featurizer
  from typing import List
  import numpy
  from numpy.typing import ArrayLike
label_desc: Use image node_id 3 for calling the RxnFeaturizer obj's underlying member method code with example usage: obj.__call__() and returns: features